(this document will be updated some as the weeks proceed)
- Also see guide to Alex Holcombe and his lectures, posted on Canvas
Week 7: Stats and studies: Correlation and causation
- Correlation and causation intro
- Learning objectives:
- Understand and apply three causal models to explaining correlations
- Know the term “spurious correlation”
- Where does data come from?
- Understanding correlation more deeply
- Correlation of X with Y same as Y with X
- Measurement and correlation
- Correlation not affected by changes in units
- When linear regression is not appropriate: COVID-19 interactive lesson
- Three causal models
- A causal model can be at any level of detail
- Explanatory, Outcome, and Nuisance variables
- IV (an explanatory variable in an experiment) and DV (an outcome variable in an experiment). Bruce slide:

- Nuisance variable
- Is not different, on average, for the different levels of the variable we are interested in
- Confound variables
- Is different for the different levels of the variable we are interested in.
- Bruce slide:

- Can you distinguish between confounds and nuisance variables?
- Random assignment
- Bruce slide:

- Randomisation and control group
- Bruce slide

- Time as a third variable
- Relationship between many observed variables, such as pirates and high average temperature, is confounded by time.
- Time: Post hoc ergo propter hoc (After this therefore because of this) fallacy
Week 8
- Dichotomous correlation
- dichotomous variables and correlation
- Contingency tables
- Later you will see the link to arguments and logic
- 4 kinds of control
- Statistical adjustment - controlling for confounds
- A controlled variable - matching
- Causal phrases verus correlational phrases, see “Distinguish correlational and causal..” video on Canvas

Week 9
- Hypothesis testing review and extension
- Review of hypothesis testing (recall tutorial week 5 and Bruce’s 18 March lecture)
- Bruce slide:

- False positives, false negatives, true positives, true negatives
- Hypothesis testing and medical testing
- Sensitivity and specificity
- Why does the news have lots of false positives?
- Excess of statistical comparisons
- More reasons for errors in science given later in the class, after logic and arguments
Arguments and logic, Weeks 9-10
We’re going to go back and forth between bare-bones examples and arguments from the wild, giving you more and more tools to deal with the real-world ones.
A 9 min intro video on youtube, but it uses different terminology than we use in this unit.
- We need to know good reasons for believing things. Ideally,
- Assumptions are true
- Logic must be airtight (inescapable)
- Syllogisms introduction
- Suppositionally inescapable; inescapable
- Truth contingency tables
- Necessary and sufficient
- Syllogisms
- Suppositionally inescapable, inescapable, suppositionally solid, solid
- Towards real-world arguments
- The vegetarianism argument, student responses, and the difficulty of decoupling
- Redundant premises
- Implicit premises
Arguments and reasoning in the wild, Weeks 11-13
Week 11
Fallacies.
Weeks 12 and 13
- Deduction and induction in science
- Analysing scientific abstracts
- Analysing the Molly and Bea argument
- Hawthorne and Expectancy effects
- Experimenter
- Participant
- Addressing expectancy effects: blinding
- Rhetoric, mere rhetoric, and informal fallacies
- Boosters and hedgers
- Post hoc ergo propter hoc, again
- Video combining this fallacy with deductive and inductive logic. Good way to bring previous bits of the class together!
- Ad populum
- Ad hominem “To the person”:
- Puerile name-calling
- Vaccilation
- Appeal to authority
- Appropriate versus inappropriate
- Straw man
- Relation to principle of charity
- Fallacy of the single cause
- Circularity
- Minimising evidence against
- Overwhelming exception
- No True Scotsman
- Slippery slope
